Grabbing SPINS gradients

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## Loading required package: ExPosition
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Read in the SPINS big table

## New names:
## Rows: 164640 Columns: 8
## -- Column specification
## -------------------------------------------------------- Delimiter: "," chr
## (4): ROI, Network, Subject, Site dbl (4): ...1, grad1, grad2, grad3
## i Use `spec()` to retrieve the full column specification for this data. i
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## * `` -> `...1`

read subject data

##  [1] "record_id"                        "scanner"                         
##  [3] "diagnostic_group"                 "demo_sex"                        
##  [5] "demo_age_study_entry"             "scog_rmet_total"                 
##  [7] "scog_er40_total"                  "scog_tasit1_total"               
##  [9] "scog_tasit2_sinc"                 "scog_tasit2_simpsar"             
## [11] "scog_tasit2_parsar"               "scog_tasit3_lie"                 
## [13] "scog_tasit3_sar"                  "np_domain_tscore_process_speed"  
## [15] "np_domain_tscore_att_vigilance"   "np_domain_tscore_work_mem"       
## [17] "np_domain_tscore_verbal_learning" "np_domain_tscore_visual_learning"
## [19] "np_domain_tscore_reasoning_ps"
## New names:
## Rows: 467 Columns: 43
## -- Column specification
## -------------------------------------------------------- Delimiter: "," chr
## (4): record_id, scanner, diagnostic_group, demo_sex dbl (36): ...1,
## demo_age_study_entry, scog_rmet_total, scog_er40_total, scog... lgl (3):
## exclude_MRI, exclude_meanFD, exclude_earlyTerm
## i Use `spec()` to retrieve the full column specification for this data. i
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## * `` -> `...1`

Check subject overlap

grad.sub <- spins_grads_wide$Subject[order(spins_grads_wide$Subject)]
behav.sub <- lol_spins_behav$record_id[order(lol_spins_behav$record_id)]

# behav.sub[behav.sub %in% grad.sub == FALSE]
# grad.sub[grad.sub %in% behav.sub == FALSE]

# complete.cases(spins_grads_wide)
# complete.cases(lol_spins_behav)
kept.sub <- lol_spins_behav$record_id[complete.cases(lol_spins_behav)==TRUE] # 420

## grab the matching data

behav.dat <- lol_spins_behav[kept.sub,c(6:19)]

spins_grads_wide_org <- spins_grads_wide[,-1]
rownames(spins_grads_wide_org) <- spins_grads_wide$Subject
grad.dat <- spins_grads_wide_org[kept.sub,]

## variables to regress out
regout.dat <- var2regout_num[kept.sub,]

Demographics

behav_all <- lol_spins_behav[kept.sub,]

table_one <- CreateTableOne(vars = colnames(behav_all)[4:19], strata="diagnostic_group",data=behav_all)

lol_demo <- 
  read_csv('../data/spins_lolivers_subject_info_for_grads_2022-04-21(withcomposite).csv') %>%
  filter(exclude_MRI==FALSE, 
         exclude_meanFD==FALSE, 
         exclude_earlyTerm==FALSE) %>% as.data.frame
## New names:
## Rows: 467 Columns: 46
## -- Column specification
## -------------------------------------------------------- Delimiter: "," chr
## (4): record_id, scanner, diagnostic_group, demo_sex dbl (39): ...1,
## demo_age_study_entry, scog_rmet_total, scog_er40_total, scog... lgl (3):
## exclude_MRI, exclude_meanFD, exclude_earlyTerm
## i Use `spec()` to retrieve the full column specification for this data. i
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## * `` -> `...1`
lol_demo$subject <- sub("SPN01_", "sub-", lol_demo$record_id) %>% sub("_", "", .)
rownames(lol_demo) <- lol_demo$record_id
lol_demo_match <- lol_demo[kept.sub,]

spins_demo <- lol_demo_match %>% 
  select(demo_sex, demo_age_study_entry, diagnostic_group, scog_rmet_total, scog_er40_total, #scog_mean_ea,
         scog_tasit1_total,
         scog_tasit2_total, scog_tasit3_total,np_composite_tscore, np_domain_tscore_att_vigilance,
         np_domain_tscore_process_speed, np_domain_tscore_work_mem,
         np_domain_tscore_verbal_learning, np_domain_tscore_visual_learning,
         np_domain_tscore_reasoning_ps, 
         #bsfs_sec2_total, bsfs_sec3_total, bsfs_sec3_total, bsfs_sec4_total, bsfs_sec5_total, bsfs_sec6_total,
         #fd_mean_rest
  ) %>% data.frame
colnames(spins_demo)
##  [1] "demo_sex"                         "demo_age_study_entry"            
##  [3] "diagnostic_group"                 "scog_rmet_total"                 
##  [5] "scog_er40_total"                  "scog_tasit1_total"               
##  [7] "scog_tasit2_total"                "scog_tasit3_total"               
##  [9] "np_composite_tscore"              "np_domain_tscore_att_vigilance"  
## [11] "np_domain_tscore_process_speed"   "np_domain_tscore_work_mem"       
## [13] "np_domain_tscore_verbal_learning" "np_domain_tscore_visual_learning"
## [15] "np_domain_tscore_reasoning_ps"
rownames(spins_demo) <- lol_demo_match$subject

spins_demo %>%
  group_by(diagnostic_group) %>%
  summarise_if(is.numeric, mean, na.rm = TRUE) %>% t
##                                  [,1]       [,2]      
## diagnostic_group                 "case"     "control" 
## demo_age_study_entry             "31.41532" "31.94767"
## scog_rmet_total                  "24.56855" "27.59649"
## scog_er40_total                  "31.83539" "33.54651"
## scog_tasit1_total                "22.50000" "24.63953"
## scog_tasit2_total                "47.52823" "54.47093"
## scog_tasit3_total                "48.35102" "54.71512"
## np_composite_tscore              "35.42387" "49.57059"
## np_domain_tscore_att_vigilance   "39.49794" "47.64912"
## np_domain_tscore_process_speed   "39.69355" "53.06395"
## np_domain_tscore_work_mem        "41.27016" "49.15698"
## np_domain_tscore_verbal_learning "40.66532" "50.30233"
## np_domain_tscore_visual_learning "38.72984" "48.37791"
## np_domain_tscore_reasoning_ps    "42.91129" "48.75581"
spins_demo %>%
  group_by(diagnostic_group) %>%
  summarize_if(is.numeric, sd, na.rm = TRUE) %>% t
##                                  [,1]        [,2]       
## diagnostic_group                 "case"      "control"  
## demo_age_study_entry             " 9.768209" "10.395267"
## scog_rmet_total                  "5.258051"  "3.821886" 
## scog_er40_total                  "4.549732"  "3.319822" 
## scog_tasit1_total                "3.640750"  "2.135267" 
## scog_tasit2_total                "8.526169"  "4.228042" 
## scog_tasit3_total                "7.271608"  "5.264379" 
## np_composite_tscore              "12.93041"  "11.01147" 
## np_domain_tscore_att_vigilance   "11.65920"  "12.71612" 
## np_domain_tscore_process_speed   "13.16429"  "10.09612" 
## np_domain_tscore_work_mem        "11.19371"  "11.36136" 
## np_domain_tscore_verbal_learning "8.937756"  "9.438716" 
## np_domain_tscore_visual_learning "12.45736"  "10.06134" 
## np_domain_tscore_reasoning_ps    "10.97108"  " 9.54391"
cbind(table(spins_demo$diagnostic_group, spins_demo$demo_sex), table(spins_demo$diagnostic_group))
##         female male    
## case        79  169 248
## control     80   92 172

Regress out the effects

table(regout.dat$demo_sex_num)
## 
##   0   1 
## 159 261
behav.reg <- apply(behav.dat, 2, function(x) lm(x~regout.dat$demo_sex + regout.dat$demo_age_study_entry + regout.dat$fd_mean_rest)$residual)

grad.reg <- apply(grad.dat, 2, function(x) lm(x~regout.dat$demo_sex + regout.dat$demo_age_study_entry + regout.dat$fd_mean_rest)$residual)

grad.reg2plot <- apply(grad.dat, 2, function(x){
  model <- lm(x~regout.dat$demo_sex + regout.dat$demo_age_study_entry + regout.dat$fd_mean_rest)
  return(model$residual + model$coefficient[1])
} )

grab some network colours

networks <- read_delim("../networks.txt", 
                       "\t", escape_double = FALSE, trim_ws = TRUE) %>%
  select(NETWORK, NETWORKKEY, RED, GREEN, BLUE, ALPHA) %>%
  distinct() %>%
  add_row(NETWORK = "Subcortical", NETWORKKEY = 13, RED = 0, GREEN=0, BLUE=0, ALPHA=255) %>%
  mutate(hex = rgb(RED, GREEN, BLUE, maxColorValue = 255)) %>%
  arrange(NETWORKKEY)
## Rows: 718 Columns: 12
## -- Column specification --------------------------------------------------------
## Delimiter: "\t"
## chr (4): LABEL, HEMISPHERE, NETWORK, GLASSERLABELNAME
## dbl (8): INDEX, KEYVALUE, RED, GREEN, BLUE, ALPHA, NETWORKKEY, NETWORKSORTED...
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
networks$hex <- darken(networks$hex, 0.2)

# oi <- networks$hex
# swatchplot(
#   "-40%" = lighten(oi, 0.4),
#   "-20%" = lighten(oi, 0.2),
#   "  0%" = oi,
#   " 20%" =  darken(oi, 0.2),
#   " 25%" =  darken(oi, 0.25),
#   " 30%" =  darken(oi, 0.3),
#   " 35%" =  darken(oi, 0.35),
#   off = c(0, 0)
# )

# networks

get row and column designs

## match ROIs to networks
ROI.network.match <- cbind(spins_grads$ROI, spins_grads$Network) %>% unique
ROI.idx <- ROI.network.match[,2]
names(ROI.idx) <- ROI.network.match[,1]
### match networks with colors
net.col.idx <- networks$hex
names(net.col.idx) <- networks$NETWORK

## design matrix for subjects
sub.dx <- spins_dx_org[kept.sub,]

diagnostic.dx <- sub.dx$diagnostic_group %>% as.matrix
diagnostic.dx <- recode(diagnostic.dx, !!!c("case" = "SSD"))
diagnostic.col.idx <- c("SSD" = "darkorchid3",
                        "control" = "gray50")
diagnostic.col <- list()
diagnostic.col$oc <- recode(diagnostic.dx, !!!diagnostic.col.idx) %>% as.matrix()
diagnostic.col$gc <- diagnostic.col.idx %>% as.matrix

## design matrix for columns - behavioral 
behav.dx <- matrix(nrow = ncol(behav.dat), ncol = 1, dimnames = list(colnames(behav.dat), "type")) %>% as.data.frame

behav.col <- c("scog" = "#D97614",#"#F28E2B",
               "np" = "#3F7538",#"#59A14F",
               "bsfs" = "#D37295")

behav.dx$type <- sub("(^[^_]+).*", "\\1", colnames(behav.dat))
behav.dx$type.col <- recode(behav.dx$type, !!!behav.col)

## design matrix for columns - gradient
grad.dx <- matrix(nrow = ncol(grad.dat), ncol = 4, dimnames = list(colnames(grad.dat), c("gradient", "ROI", "network", "network.col"))) %>% as.data.frame

grad.dx$gradient <- sub("(^[^.]+).*", "\\1", colnames(grad.dat))
grad.dx$ROI <- sub("^[^.]+.(*)", "\\1", colnames(grad.dat))
grad.dx$network <- recode(grad.dx$ROI, !!!ROI.idx)
grad.dx$network.col <- recode(grad.dx$network, !!!net.col.idx)

## get different alpha for gradients
grad.col.idx <- c("grad1" = "grey30",
                  "grad2" = "grey60",
                  "grad3" = "grey90")
grad.dx$gradient.col <- recode(grad.dx$gradient, !!!grad.col.idx)

## for heatmap
col.heat <- colorRampPalette(c("red", "white", "blue"))(256)

Run PLS-C

pls.res <- tepPLS(behav.reg, grad.reg, DESIGN = sub.dx$diagnostic_group, make_design_nominal = TRUE, graphs = FALSE)

pls.boot <- data4PCCAR::Boot4PLSC(behav.reg, grad.reg, scale1 = "SS1", scale2 = "SS1", nIter = 1000, nf2keep = 5, eig = TRUE)
## Registered S3 method overwritten by 'data4PCCAR':
##   method                  from     
##   print.str_colorsOfMusic PTCA4CATA
pls.boot$bootRatiosSignificant.j[abs(pls.boot$bootRatios.j) < 2.88] <- FALSE
pls.boot$bootRatiosSignificant.i[abs(pls.boot$bootRatios.i) < 2.88] <- FALSE

pls.inf <- data4PCCAR::perm4PLSC(behav.reg, grad.reg, scale1 = "SS1", scale2 = "SS1", nIter = 1000)
# ## swith direction for dimension 3
pls.res$TExPosition.Data$fi[,1] <- pls.res$TExPosition.Data$fi[,1]*-1
pls.res$TExPosition.Data$fj[,1] <- pls.res$TExPosition.Data$fj[,1]*-1
pls.res$TExPosition.Data$pdq$p[,1] <- pls.res$TExPosition.Data$pdq$p[,1]*-1
pls.res$TExPosition.Data$pdq$q[,1] <- pls.res$TExPosition.Data$pdq$q[,1]*-1
pls.res$TExPosition.Data$lx[,1] <- pls.res$TExPosition.Data$lx[,1]*-1
pls.res$TExPosition.Data$ly[,1] <- pls.res$TExPosition.Data$ly[,1]*-1

## Scree plot
PlotScree(pls.res$TExPosition.Data$eigs, 
          p.ev = pls.inf$pEigenvalues)

## Print singular values
pls.res$TExPosition.Data$pdq$Dv
##  [1] 7.1330565 2.1157103 1.9079739 1.6310932 1.5321817 1.4132437 1.2515730
##  [8] 1.1846931 1.1177672 1.0190911 0.9319565 0.9106204 0.8248812 0.7818378
## Print eigenvalues
pls.res$TExPosition.Data$eigs
##  [1] 50.8804950  4.4762303  3.6403644  2.6604652  2.3475808  1.9972578
##  [7]  1.5664349  1.4034979  1.2494036  1.0385467  0.8685430  0.8292296
## [13]  0.6804289  0.6112703
pls.res$TExPosition.Data$t
##  [1] 68.5261515  6.0286134  4.9028643  3.5831302  3.1617357  2.6899186
##  [7]  2.1096838  1.8902392  1.6827041  1.3987209  1.1697588  1.1168113
## [13]  0.9164057  0.8232624
## Compare the inertia to the largest possible inertia
sum(cor(behav.dat, grad.dat)^2)
## [1] 81.59259
sum(cor(behav.dat, grad.dat)^2)/(ncol(behav.dat)*ncol(grad.dat))
## [1] 0.004955818

Here, we show that the effect that PLSC decomposes is pretty small to begin with. The effect size of the correlation between the two tables is 92.40 which accounts for 0.0065 of the largest possible effect.

Results

Dimension 1

lxly.out[[1]]

lx1.ssd <- pls.res$TExPosition.Data$lx[which(sub.dx$diagnostic_group == "case"), 1]
lx1.hc <- pls.res$TExPosition.Data$lx[which(sub.dx$diagnostic_group == "control"), 1]

gridExtra::grid.arrange(bar.grad1, bar.grad2, bar.grad3, ncol = 1)

PrettyBarPlot2(pls.res$TExPosition.Data$fi[,1],
               threshold = 0, 
               color4bar = ifelse(pls.boot$bootRatiosSignificant.i[,1] == TRUE, behav.dx$type.col, "grey90"),
               horizontal = FALSE, main = "Scores - behavioural")
## Warning: Vectorized input to `element_text()` is not officially supported.
## i Results may be unexpected or may change in future versions of ggplot2.
cor.heat <- pls.res$TExPosition.Data$X %>% heatmap(col = col.heat)

## control
grad.dat.ctrl <- grad.dat[sub.dx$diagnostic_group == "control",]
behav.dat.ctrl <- behav.dat[sub.dx$diagnostic_group == "control",]
corX.ctrl <- cor(as.matrix(behav.dat.ctrl),as.matrix(grad.dat.ctrl))
heatmap(corX.ctrl[cor.heat$rowInd, cor.heat$colInd], col = col.heat, Rowv = NA, Colv = NA)

## case
grad.dat.case <- grad.dat[sub.dx$diagnostic_group == "case",]
behav.dat.case <- behav.dat[sub.dx$diagnostic_group == "case",]
corX.case <- cor(as.matrix(behav.dat.case),as.matrix(grad.dat.case))
heatmap(corX.case[cor.heat$rowInd, cor.heat$colInd], col = col.heat, Rowv = NA, Colv = NA)

Bootstrap confidence intervals on the first LV

\[CV_{control} = \] 1.05 % bootstrap CI: [0.81, 1.36]

\[CV_{SSD} = \] 2.35 % bootstrap CI: [1.82, 3.1]

Dimension 2

lxly.out[[2]]

gridExtra::grid.arrange(bar.grad1, bar.grad2, bar.grad3, ncol = 1)

PrettyBarPlot2(pls.res$TExPosition.Data$fi[,2],
               threshold = 0, color4bar = ifelse(pls.boot$bootRatiosSignificant.i[,2] == TRUE, behav.dx$type.col, "grey90"), 
               horizontal = FALSE, main = "Scores - behavioural")

dim1.est <- pls.res$TExPosition.Data$pdq$Dv[1]*as.matrix(pls.res$TExPosition.Data$pdq$p[,1], ncol = 1) %*% t(as.matrix(pls.res$TExPosition.Data$pdq$q[,1], ncol = 1))


cor.heat.res1 <- (pls.res$TExPosition.Data$X - dim1.est) %>% heatmap(col = col.heat)

Dimension 3

lxly.out[[3]]

gridExtra::grid.arrange(bar.grad1, bar.grad2, bar.grad3, ncol = 1)

PrettyBarPlot2(pls.res$TExPosition.Data$fi[,3],
               threshold = 0, color4bar = ifelse(pls.boot$bootRatiosSignificant.i[,3] == TRUE, behav.dx$type.col, "grey90"),
               horizontal = FALSE, main = "Scores - behavioural")

dim2.est <- (as.matrix(pls.res$TExPosition.Data$pdq$p[,1:2]) %*% pls.res$TExPosition.Data$pdq$Dd[1:2,1:2] %*% t(as.matrix(pls.res$TExPosition.Data$pdq$q[,1:2])))


cor.heat.res2 <- heatmap(pls.res$TExPosition.Data$X - dim2.est, col = col.heat)

Dimension 4

lxly.out[[4]]

gridExtra::grid.arrange(bar.grad1, bar.grad2, bar.grad3, ncol = 1)

PrettyBarPlot2(pls.res$TExPosition.Data$fi[,4],
               threshold = 0, 
               color4bar = ifelse(pls.boot$bootRatiosSignificant.i[,4] == TRUE, behav.dx$type.col, "grey90"),
               horizontal = FALSE, main = "Scores - behavioural")


dim3.est <- (as.matrix(pls.res$TExPosition.Data$pdq$p[,1:3]) %*% pls.res$TExPosition.Data$pdq$Dd[1:3,1:3] %*% t(as.matrix(pls.res$TExPosition.Data$pdq$q[,1:3])))


cor.heat.res3 <- heatmap(pls.res$TExPosition.Data$X - dim3.est, col = col.heat)

Back into the brain

Dimension 1

## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'

Dimension 2

## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'

Dimension 3

## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'

Dimension 4

## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'

Group difference and fancy figures

Cohen’s

## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'

Group difference

visualization

Group effect

By contributions and significance to PLS

## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'

Subcortical

Group effect

By contributions and significance to PLS

Relationship to symptoms

Correlation to composite scores

## Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
## EOF within quoted string

Correlation to individual scores